Systems and Methods for Processing Electronic Images to Evaluate Medical Images

1. A method for personalized non-invasive assessment of artery stenosis for a patient, comprising: receiving medical image data of at least a part of the patient's vascular system that includes one or more arteries of the patient; extracting patient-specific arterial geometry of the patient from the received medical image data; performing at least one simulation on the patient-specific arterial geometry of the patient to extract at least one feature from the patient-specific arterial geometry of the patient, the at least one feature including a simulated value of at least one blood flow characteristic; automatically computing one or more index values of physiologic values corresponding to the at least one blood flow characteristic of the at least one extracted feature for one or more locations of interest in the patient-specific arterial geometry by inputting the at least one extracted feature into a trained machine-learning based model, wherein: the trained machine-learning based model is trained to learn associations between one or more extracted features and one or more physiological values corresponding to the one or more extracted features, the one or more physiological values arising from one or both of (i) one or more measurements taken from individuals other than the patient or (ii) one or more simulation based on anatomy of the individuals other than the patient; the at least one feature extracted from the patient-specific arterial geometry of the patient corresponds to the one or more features used to train the machine-learning model; and the machine-learning model is configured, via the training, to generate, as output, the one or more index values of the one or more physiological values for one or more locations of interest in the patient-specific arterial geometry in response to one or more input extracted features, based on the learned associations.

2. The method of claim 1, further comprising: receiving at least one of non-invasive physiological measurements of the patient or demographic data of the patient, wherein computing the one or more index values is further based on features corresponding to the at least one of the non-invasive physiological measurements of the patient or the demographic data of the patient.

3. The method of claim 1, further comprising: receiving one or more parameters of the patient, wherein computing the one or more index values is further based on one or more features corresponding to the one or more parameters of the patient.

4. The method of claim 1, wherein: the physiologic values are physiologic blood pressure values, and the trained machine-learning based model is trained based on one or more patient-specific models of blood flow circulation.

5. The method of claim 1, wherein extracting features from the patient-specific arterial geometry of the patient includes extracting a plurality of geometric measurements for one or more artery stenosis regions in the patient-specific arterial geometry of the patient.

6. The method of claim 1, wherein the one or more locations of interest correspond to one or more artery stenosis locations.

7. The method of claim 1, wherein: the physiologic values are physiologic blood pressure values; and the one or more index values are one or more fractional flow reserve (FFR) values.

8. The method of claim 1, wherein: the trained machine-learning based model is one of a plurality of trained machine-learning based models, each trained based on a respective set of one or more features, and computing the one or more index values includes computing a plurality of hemodynamic indices of the physiologic values for each of the one or more locations of interest in the patient-specific arterial geometry based on each extracted feature using the plurality of trained machine-learning models corresponding to the extracted feature.

9. The method of claim 1, further comprising: displaying a visualization of the patient-specific arterial geometry that is color coded based on the one or more index values computed for the one or more locations of interest in the patient-specific arterial geometry.

10. A system for personalized non-invasive assessment of artery stenosis for a patient, comprising: at least one data storage device storing instructions for personalized non-invasive assessment of artery stenosis for a patient in an electronic storage medium; and at least one processor configured to execute the instructions to perform operations including: receiving medical image data of at least a part of the patient's vascular system that includes one or more arteries of the patient; extracting patient-specific arterial geometry of the patient from the received medical image data; performing at least one simulation on the patient-specific arterial geometry of the patient to extract at least one feature from the patient-specific arterial geometry of the patient, the at least one feature including a simulated value of at least one blood flow characteristic; automatically computing one or more index values of physiologic values corresponding to the at least one blood flow characteristic of the at least one extracted feature for one or more locations of interest in the patient-specific arterial geometry by inputting the at least one extracted feature into a trained machine-learning based model, wherein: the trained machine-learning based model is trained to learn associations between one or more extracted features and one or more physiological values corresponding to the one or more extracted features, the one or more physiological values arising from one or both of (i) one or more measurements taken from individuals other than the patient or (ii) one or more simulation based on anatomy of the individuals other than the patient; the at least one feature extracted from the patient-specific arterial geometry of the patient corresponds to the one or more features used to train the machine-learning model; and the machine-learning model is configured, via the training, to generate, as output, the one or more index values of the one or more physiological values for one or more locations of interest in the patient-specific arterial geometry in response to one or more input extracted features, based on the learned associations.

11. The system of claim 10, wherein the operations further including: receiving at least one of non-invasive physiological measurements of the patient or demographic data of the patient, wherein computing the one or more index values using the trained machine-learning based model is further based on features corresponding to at least one of the non-invasive physiological measurements of the patient or the demographic data of the patient.

12. The system of claim 10, wherein the operations further including: receiving one or more parameters of the patient, wherein computing the one or more index values using the trained machine-learning based model is further based on features corresponding to the one or more parameters of the patient.

13. The system of claim 10, wherein: the physiologic values are physiologic blood pressure values; and the trained machine-learning based model is trained based on one or more patient-specific multi-scale models of blood flow circulation.

14. The system of claim 10, wherein: the physiologic values are physiologic blood pressure values; and the one or more index values are one or more fractional flow reserve (FFR) values.

15. The system of claim 10, wherein: the trained machine-learning based model is one of a plurality of trained machine-learning based models, each trained based on a respective set of one or more features; and computing the one or more index values includes computing a plurality of hemodynamic indices of the physiologic values for each of the one or more locations of interest in the patient-specific arterial geometry based on each extracted feature using the plurality of trained machine-learning models corresponding to the extracted feature.

16. The system of claim 10, wherein the operations further include: displaying a visualization of the patient-specific arterial geometry that is color coded based on the one or more index values computed for the one or more locations of interest in the patient-specific arterial geometry.

17. A non-transitory computer-readable medium, for personalized non-invasive assessment of artery stenosis for a patient, storing operations including: receiving medical image data of at least a part of the patient's vascular system that includes one or more arteries of the patient; extracting patient-specific arterial geometry of the patient from the received medical image data; performing at least one simulation on the patient-specific arterial geometry of the patient to extract at least one feature from the patient-specific arterial geometry of the patient, the at least one feature including a simulated value of at least one blood flow characteristic; automatically computing one or more index values of physiologic values corresponding to the at least one blood flow characteristic of the at least one extracted feature for one or more locations of interest in the patient-specific arterial geometry by inputting the at least one extracted feature into a trained machine-learning based model, wherein: the trained machine-learning based model is trained to learn associations between one or more extracted features and one or more physiological values corresponding to the one or more extracted features, the one or more physiological values arising from one or both of (i) one or measurements taken from individuals other than the patient or (ii) one or more simulation based on anatomy of the individuals other than the patient; the at least one feature extracted from the patient-specific arterial geometry of the patient corresponds to the one or more features used to train the machine-learning model; and the machine-learning model is configured, via the training, to generate, as output, the one or more index values of the one or more physiological values for one or more locations of interest in the patient-specific arterial geometry in response to one or more input extracted features, based on the learned associations.

18. The non-transitory computer-readable medium of claim 17, the operations further including: receiving at least one of non-invasive physiological measurements of the patient or demographic data of the patient, wherein computing the one or more index values using the trained machine-learning based model is further based on features corresponding to at least one of the non-invasive physiological measurements of the patient or the demographic data of the patient.

19. The non-transitory computer-readable medium of claim 17, the operations further including: receiving one or more parameters of the patient, wherein computing the one or more index values using the trained machine-learning based model is further based on features corresponding to one or more parameters of the patient.